Sensors 2011, 11(5), 4483-4511; doi:10.3390/s110504483
Article

Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems

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Received: 17 March 2011; Accepted: 18 April 2011 / Published: 27 April 2011
(This article belongs to the Special Issue Adaptive Sensing)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive experiment design (DEED) regularization paradigm are unified into a new dynamic experiment design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.
Keywords: adaptive sensing; experiment design; radar imaging; sensor system; spatial spectrum pattern (SSP); synthetic aperture radar (SAR); regularization; variational analysis
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MDPI and ACS Style

Shkvarko, Y.; Tuxpan, J.; Santos, S. Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems. Sensors 2011, 11, 4483-4511.

AMA Style

Shkvarko Y, Tuxpan J, Santos S. Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems. Sensors. 2011; 11(5):4483-4511.

Chicago/Turabian Style

Shkvarko, Yuriy; Tuxpan, José; Santos, Stewart. 2011. "Dynamic Experiment Design Regularization Approach to Adaptive Imaging with Array Radar/SAR Sensor Systems." Sensors 11, no. 5: 4483-4511.

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